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Post-event evaluation of residual capacity of building structures based on seismic monitoring

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Abstract

Structural capacity evaluation is essential to support the safety assessment and decision-making process of existing building structures after disastrous earthquakes. Current post-earthquake evaluation practices rely more on manual on-site inspections, which are labor-intensive and subjective. A simulation-based capacity evaluation could be a desired alternative when numerical models for these buildings are prior-identified and updated using structural health monitoring data. This study proposes a procedure for identifying the capacity curve and assessing the residual capacity of existing structures using seismic monitoring data. The mass-normalized spectral acceleration-displacement (AD format) relation is first defined in a single-degree-of-freedom system. Considering the post-event deterioration of structural capacity, a data-driven reduction factor for the capacity curve is introduced to quantify the potential structural degradation. With the aid of the updated capacity curve, the residual capacity of the earthquake-damaged structure is then predicted via incremental dynamic analysis. The feasibility and accuracy of the proposed method are analyzed via numerical simulations and further validated using a large-scale shaking table test and a real-world instrumented building. Results show that the proposed method could identify the capacity curve of the existing structure from seismic monitoring data and estimate the hysteresis responses with a favorable agreement. It could provide the residual capacity of the target structure and quantify its capacity reduction, which can informatively facilitate the post-earthquake structural safety management.

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Data Availability

Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Shegay AV, Miura K, Fujita K, Tabata Y, Maeda M, Seki M (2023) Evaluation of seismic residual capacity ratio for reinforced concrete structures. Resilient Cities Struct 2:28–45

    Article  Google Scholar 

  2. JBDPA (2001) Standard for seismic evaluation of existing reinforced concrete buildings. Japan Building Disaster Prevention Association, Berlin

    Google Scholar 

  3. Guillier B, Chatelain JL, Tavera H, Perfettini H, Ochoa A, Herrera B (2014) Establishing empirical period formula for RC buildings in Lima, Peru: evidence for the impact of both the 1974 Lima earthquake and the application of the Peruvian seismic code on high-rise buildings. Seismol Res Lett 85:1308–1315

    Article  Google Scholar 

  4. Skolnik D, Lei Y, Yu E, Wallace JW (2019) Identification, model updating, and response prediction of an instrumented 15-story steel-frame building. Earthq Spectra 22:781–802

    Article  Google Scholar 

  5. Han Q, Ma Q, Xu J, Liu M (2021) Structural health monitoring research under varying temperature condition: a review. J Civ Struct Heal Monit 11:149–173

    Article  Google Scholar 

  6. Zhou K, Li Q (2021) Effects of time-variant modal frequencies of high-rise buildings on damping estimation. Earthq Eng Struct Dyn 50:394–414

    Article  Google Scholar 

  7. Zeng J, Xie Y-L, Kim YH, Wang J (2023) Automation in Bayesian operational modal analysis using clustering-based interpretation of stabilization diagram. J Civ Struct Heal Monit 13:443–467

    Article  Google Scholar 

  8. Limongelli MP (2014) Seismic health monitoring of an instrumented multistory building using the interpolation method. Earthq Eng Struct Dyn 43:1581–1602

    Article  Google Scholar 

  9. Roohi M, Hernandez Eric M, Rosowsky D (2021) Reconstructing element-by-element dissipated hysteretic energy in instrumented buildings: application to the Van Nuys Hotel testbed. J Eng Mech 147:04020141

    Article  Google Scholar 

  10. Chou JY, Chang CM (2022) Low-story damage detection of buildings using deep neural network from frequency phase angle differences within a low-frequency band. J Build Eng 55:104692

    Article  Google Scholar 

  11. Oh BK, Jung WC, Park HS (2023) Artificial intelligence-based damage localization method for building structures using correlation of measured structural responses. Eng Appl Artif Intell 121:106019

    Article  Google Scholar 

  12. Catbas FN, Susoy M, Frangopol DM (2008) Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data. Eng Struct 30:2347–2359

    Article  Google Scholar 

  13. Tarozzi M, Pignagnoli G, Benedetti A (2022) Evaluation of the residual carrying capacity of a large-scale model bridge through frequency shifts. J Civ Struct Heal Monit 12:931–941

    Article  Google Scholar 

  14. Rune Brincker CEV (2015) Introduction to operational modal analysis, pp 1–16

  15. Guo Y, Kwon Dae K, Kareem A (2016) Near-real-time hybrid system identification framework for civil structures with application to Burj Khalifa. J Struct Eng 142:04015132

    Article  Google Scholar 

  16. Zhang Q, Tang XWJ, Yang B (2019) Online automatic structural health assessment of the Shanghai Tower. Smart Struct Syst 24:319–332

    Google Scholar 

  17. Li Z, Park HS, Adeli H (2017) New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms. Struct Design Tall Spec Build 26:e1312

    Article  Google Scholar 

  18. Lu J, Xie Q, Zhu W (2023) Seismic damage detection of ultra-high voltage transformer bushings using output-only acceleration responses. J Civ Struct Health Monit

  19. Azimi M, Eslamlou AD, Pekcan G (2020) Data-driven structural health monitoring and damage detection through deep learning: state-of-the-art review. Sensors. https://doi.org/10.3390/s20102778

    Article  Google Scholar 

  20. Zhang T, Xu W, Wang S, Du D, Tang J (2024) Seismic response prediction of a damped structure based on data-driven machine learning methods. Eng Struct 301:117264

    Article  Google Scholar 

  21. Malekloo A, Ozer E, AlHamaydeh M, Girolami M (2021) Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Struct Health Monit 21:1906–1955

    Article  Google Scholar 

  22. Asgarieh E, Moaveni B, Barbosa AR, Chatzi E (2017) Nonlinear model calibration of a shear wall building using time and frequency data features. Mech Syst Signal Process 85:236–251

    Article  Google Scholar 

  23. Shan J, Zhang H, Shi W, Lu X (2020) Health monitoring and field-testing of high-rise buildings: a review. Struct Concr 21:1272–1285

    Article  Google Scholar 

  24. Absi GN, Mahadevan S (2016) Multi-fidelity approach to dynamics model calibration. Mech Syst Signal Process 68–69:189–206

    Article  Google Scholar 

  25. Yu X, Li S, Lu D, Tao J (2020) Collapse capacity of inelastic single-degree-of-freedom systems subjected to mainshock-aftershock earthquake sequences. J Earthq Eng 24:803–826

    Article  Google Scholar 

  26. Chopra AK, Goel RK, Chintanapakdee C (2003) Statistics of single-degree-of-freedom estimate of displacement for pushover analysis of buildings. J Struct Eng 129:459–469

    Article  Google Scholar 

  27. Sohn J, Choi I, Kim J (2020) Seismic performance evaluation by demand capacity ratio in vertical irregular buildings. In: The 17th World Conference on Earthquake Engineering 2020, pp 2j-0028

  28. Freeman SA, Gilmartin UM, Searer G (1999) Using strong motion recordings to construct pushover curves. In: Proceedings of 8th Canadian Conference on earthquake engineering

  29. Dowgala J, Irfanoglu A (2016) A Method for extracting building empirical capacity curves from earthquake response data. Earthq Spectra 32:2229–2244

    Article  Google Scholar 

  30. Luna BN (2009) On development of base shear versus roof drift curves using earthquake-response data. Purdue University, Ann Arbor

    Google Scholar 

  31. Pan H, Kusunoki K (2018) A wavelet transform-based capacity curve estimation approach using seismic response data. Struct Control Health Monit 25:e2267

    Article  Google Scholar 

  32. Ji X, Zhuang Y, Miao Z, Cheng Y (2023) Vision-based seismic damage detection and residual capacity assessment for an RC shaking table test structure. Earthq Eng Struct Dyn 52:806–827

    Article  Google Scholar 

  33. Miari M, Jankowski R (2022) Incremental dynamic analysis and fragility assessment of buildings founded on different soil types experiencing structural pounding during earthquakes. Eng Struct 252:113118

    Article  Google Scholar 

  34. Bao X, Zhai C-H, Zhang M-H, Xu L-J (2020) Seismic capacity assessment of postmainshock damaged containment structures using nonlinear incremental dynamic analysis. Struct Design Tall Spec Build 29:e1706

    Article  Google Scholar 

  35. Berry M, Parrish M, Eberhard M (2004) PEER structural performance database user’s manual (version 1.0). University of California, Berkeley

    Google Scholar 

  36. Shan J, Zhang H, Ouyang Y, Shi W (2020) Data-driven damage tracking and hysteresis evaluation of earthquake-excited structures with test validation. Eng Struct 207:110214

    Article  Google Scholar 

  37. Luo H, Paal SG (2018) Machine learning–based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. J Comput Civ Eng 32:04018042

    Article  Google Scholar 

  38. Lorenzo D, Reuland Y (2019) Assessing the residual capacity of buildings for post-earthquake asset management at urban scale. Valori e Valutazioni

  39. Fajfar P (2000) A nonlinear analysis method for performance-based seismic design. Earthq Spectra 16:573–592

    Article  Google Scholar 

  40. ASCE (2013) Seismic evaluation and retrofit of existing buildings. ASCE/SEI 41-13, American Society of Civil Engineers

  41. McKenna F (2011) OpenSees: a framework for earthquake engineering simulation. Computing in Science & Engineering 13:58–66

    Article  Google Scholar 

  42. Shan J, Wang L, Loong CN, Zhou Z (2023) Rapid seismic performance evaluation of existing frame structures using equivalent SDOF modeling and prior dynamic testing. J Civ Struct Heal Monit 13:749–766

    Article  Google Scholar 

  43. Takeda T, Sozen MA, Nielsen NN (1970) Reinforced concrete response to simulated earthquakes. J Struct Div 96:2557–2573

    Article  Google Scholar 

  44. Fang C, Spencer BF, Xu J, Tan P, Zhou F (2019) Optimization of damped outrigger systems subject to stochastic excitation. Eng Struct 191:280–291

    Article  Google Scholar 

  45. Todorovska MI, Trifunac MD (2008) Impulse response analysis of the Van Nuys 7-storey hotel during 11 earthquakes and earthquake damage detection. Struct Control Health Monit 15:90–116

    Article  Google Scholar 

  46. Shan J, Chen X, Yuan H, Shi W (2015) Interstory drift estimation of nonlinear structure using acceleration measurement with test validation. J Eng Mech 141:04015032

    Article  Google Scholar 

  47. Wang J, Lin C, Yen S (2007) A story damage index of seismically-excited buildings based on modal frequency and mode shape. Eng Struct 29:2143–2157

    Article  Google Scholar 

Download references

Funding

This study is sponsored by the National Natural Science Foundation of China (Grant: 52278312), and the Shanghai Qi Zhi Institute (Grant: SQZ202310).

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Correspondence to Jiazeng Shan.

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Wang, L., Shan, J. Post-event evaluation of residual capacity of building structures based on seismic monitoring. J Civil Struct Health Monit (2024). https://doi.org/10.1007/s13349-024-00803-y

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